Search-Based Prediction of Fault Count Data
by W. Afzal and R. Torkar and R. Feldt
PDF
Symbolic regression, an application domain of genetic
programming (GP), aims to find a function whose output has some desired property, like matching target values of a particular data set. While typical regression involves finding the coefficients of a pre-deļ¬ned function,
symbolic regression finds a general function, with coefficients, fitting the given set of data points. The concepts
of symbolic regression using genetic programming can
be used to evolve a model for fault count predictions.
Such a model has the advantages that the evolution is
not dependent on a particular structure of the model
and is also independent of any assumptions, which are
common in traditional time-domain parametric software reliability growth models. This research aims at
applying experiments targeting fault predictions using
genetic programming and comparing the results with
traditional approaches to compare efficiency gains.
Bibtex
@Article{Afzal2009SSBSE,
author = "Wasif Afzal and Richard Torkar and Robert Feldt",
title = "Search-Based Prediction of Fault Count Data",
year = "2009",
editor = "Massimiliano {Di Penta} and Simon Poulding",
booktitle = "Proceedings 1st International Symposium on Search Based Software Engineering SSBSE 2009",
month = "May 13-15",
address = "Windsor, UK",
publisher = "IEEE",
isbn = "978-0-7695-3675-0",
keywords = "Genetic algorithms; Genetic programming; Search-Based Software Engineering",
url = "http://www.cse.chalmers.se/~feldt/publications/afzal_2009_ssbse.html",
url = "http://www.cse.chalmers.se/~feldt/publications/afzal_2009_ssbse.pdf",
}